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A Comparative Analysis of SMS Spam Detection employing Machine Learning Methods 6

A Comparative Analysis of SMS Spam Detection employing Machi...

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6th International Conference on Computing Methodologies and communication, ICCMC 2022

作者： Aliza, Humaira Yasmin Nagary, Kazi Aahala Ahmed, Eshtiak Puspita, Kazi Mumtahina Rimi, Khadiza Akter Khater, Ankit Faisal, Fahad Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Tampere University Faculty of Information Technology and Communication Sciences Tampere Finland Jadavpur University Department of Computer Science and Engineering India

ISBN: (纸本)9781665410281

In recent times, the increment of mobile phone usage has resulted in a huge number of spam messages. Spammers continuously apply more and more new tricks that cause managing or preventing spam messages a challenging task. The aim of this study is to detect spam message to prevent different cybercrimes as spam messages have become a security threat nowadays. In this paper, studies on SMS spam problems to perform a better accuracy using several different techniques such as Support Vector Machine, K-Nearest Neighbor, Naïve Bayes, Random Forest, Logistic Regression and some more are performed. The result indicated that Support Vector Machine achieved the highest accuracy of 99%, indicating it might be useful as an effective machine learning system for future research. © 2022 IEEE.

关键词： Support vector machines

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Phases Distortion Investigation in Beyond 5G mm-Wave OFDM

Phases Distortion Investigation in Beyond 5G mm-Wave OFDM

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Kale, Kiran Raja Praveen, K.N. Savita Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350357769

Fifth-generation wireless networks are ready to handle the increasing volume of mobile traffic and provide critical technological solutions to suit various service needs. Utilizing the millimeter-wave (mm-wave) spectrum is a crucial strategy for achieving large data speeds. Additionally, analogue radio-over-fiber (ARoF) has a wide range of benefits, such as affordability, little power usage, and exceptional spectrum efficiency. As a result, mm-wave ARoF becomes a strong candidate for providing becomes the standard waveform for 5G, it is crucial that mm-wave ARoF systems support it. However, in mm-wave OFDM ARoF systems, an existence of phase interference represents a substantial performance-limiting constraint. Through a painstakingly constructed test setup, we perform a thorough examination of phase noise in this study. We are able to systematically modify the system's overall last phase noise level thanks to this configuration. In addition, we provide a fresh and unique approach for OFDM transmitter phase noise compensation. Our testing confirms the algorithm's effectiveness under these circumstances and highlights the potential advantages of mm-wave OFDM ARoF for 5G to meet by evaluating how well it works over a range of period noise values and subcarrier spacings. © 2023 IEEE.

关键词： Analogue Radio-over-Fiber Beyond 5G Distortion Frequency-Selective Channels Investigation Millimeter-Wave MM-Wave OFDM Phases Spectral Efficiency and Signal-to-Noise Ratio Subcarrier Spacings

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Reassessing Deep Learning in Time Series: Predicting Time with Foresight

Reassessing Deep Learning in Time Series: Predicting Time wi...

引用

2023 International Conference on Advances in Computation, communication and Information Technology, ICAICCIT 2023

作者： Vinutha, C.B. Vivek, V. Pandeya, Megha Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India

ISBN: (纸本)9798350344387

Machine learning relies heavily on time series forecasting, which has a wide range of uses, including although not limited to forecasting air quality, traffic patterns, and power use. Recent developments in deep training and matrix factorization models, however, have shown competitive performance and are emerging as alternatives. However, a fundamental disadvantage of these contemporary models over more established ones is their complexity. The Gradient Boosted Regression Tree, a well-known machine learning baseline, and popular deep learning models are compared in this work. We convert the time series forecast challenge into a window-based prediction issue, which is akin to (DNN) models. In addition, we design the GBRT model's input and output structure. In order to construct a single input instance to simulate a multi-output GBRT model, we concatenate the desired values with additional characteristics for each training window before flattening them. Using nine datasets, we undertake a thorough investigation and evaluate eight cutting-edge deep learning models recently presented at prestigious conferences. The findings show that a simple GBRT model performs much better than the state-of-the-art DNN models evaluated in this research thanks to the window-based input transformation. © 2023 IEEE.

关键词： Deep Learning DNN and Temporal Regularized Matrix Factorization Feature engineering Gradient Boosted Regression Trees (GBRT) Time Series Forecasting

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Effective Co-Operation in Edge-Cloud Computation Networking

Effective Co-Operation in Edge-Cloud Computation Networking

引用

2023 International Conference on Advances in Computation, communication and Information Technology, ICAICCIT 2023

作者： Mahesh, T.R. Savita Bhagwat, Renuka Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka 562112 India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350344387

To improve the performance of mobile devices, both the edge of the network and the cloud provide computing services. While the cloud can manage large processing needs, the edge can minimize latency by offering local computing services. Their cooperation is essential for edge-cloud computational business models since it increases the usage of computing resources and assures Quality of Service (QoS). To facilitate resource sharing between the network's edge and the cloud, this study proposes an effective architecture for mobility edge-cloud computing networks. It uses wholesale and buyback techniques. Study conducted design resource management issues for edge servers to manage wholesale and buyback agreements and for cloud services to establish wholesale rates and distribute local computing resources in order to maximize this sharing process. From the perspectives of both optimizing societal welfare and maximizing profitability in both the perimeter and the cloud, study conducted approach these issues. Study conducted illustrate the concavity of the social welfare for i) and suggest an ideal cloud resource management approach to maximize it. Study conducted first develop the conical shape of wholesale computing power with respect to the wholesale rate for option ii), given the difficulty of directly demonstrating the conical shape of the key issue. Study conducted then create an optimal pricing while computing administration approach for maximizing the resulting profits. According to numerical analyses, maximizing overall profit is consistent with promoting societal welfare, whereas maximizing individual earnings is achieved through maximizing pricing using cloud computing resource management. © 2023 IEEE.

关键词： Cloud computing Edge-cloud computation Mobile Edge Computing (MEC) Modelling and Performance analysis Profit transfers Profitability Quality of Service (QoS) Queueing mechanism Resource management Resource sharing social welfare

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Fostering Smart Agriculture: Using Vision-Based AI for Livestock Managing

Fostering Smart Agriculture: Using Vision-Based AI for Lives...

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2023 International Conference on Advances in Computation, communication and Information Technology, ICAICCIT 2023

作者： Shukla, Gaurav Gowri, K. Sudhamsu, Gadug Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Jain University Faculty of Engineering and Technology Department of Computer Science Engineering India

ISBN: (纸本)9798350344387

The swine industry constitutes a significant segment within livestock farming. However, conventional methods employed for monitoring pig health and welfare are laborious and time-intensive. Relying on these traditional monitoring techniques becomes increasingly taxing for both pigs and farmers, particularly in the face of the expanding scale of hog farming. This stress, in turn, impacts pig welfare and overall economic productivity. Integrating artificial intelligence (AI) has emerged as a fundamental component in the realm of intelligent pig farming. Advanced pig farming systems leverage AI integration to continuously monitor biometric data encompassing pig vocalizations and behavior. This data, acquired through sensors such as cameras and RF ID devices, is transformed into pivotal indicators of pig health and welfare. Analyzing these essential variables enables the early identification of health issues affecting pigs, facilitating prompt interventions and treatments that significantly enhance production efficiency and financial outcomes in pig farming. This paper presents a comprehensive overview, highlighting the challenges and opportunities within precision pig husbandry. The fusion of AI technologies with traditional farming practices presents novel prospects and hurdles, shaping the landscape of modern pig farming. Through this evaluation, the paper aims to underscore the transformative potential and the evolving landscape of precision-based pig farming. © 2023 IEEE.

关键词： Artificial intelligence (AI) Livestock management Pig farming Pig health Precision farming and Behavioral analysis Sensors Smart agriculture Vision-based AI Welfare monitoring

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Translational Deep Learning-Based Diagnostics: Enhancing Pathology With AI

Translational Deep Learning-Based Diagnostics: Enhancing Pat...

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2023 International Conference on Advances in Computation, communication and Information Technology, ICAICCIT 2023

作者： Ashwini, B. Sinha, Deepak Kumar Chaudhary, Chetan Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India

ISBN: (纸本)9798350344387

Transformative innovation in healthcare is led by the constantly changing field of deep learning techniques, especially designed for cellular imaging in diagnostic pathology. This paper provides a thorough examination of several deep learning approaches in the field of pathology, highlighting new uses and tackling important issues that are essential to the field's progress. The successful deployment of AI in clinical settings depends on the seamless integration of these apps into processes in digital pathology. This all-inclusive diagnostic method not only simplifies processes but also provides pathologists with cutting-edge instruments that accelerate work, improve diagnosis accuracy, and lower mistakes. The US has recently approved a limited number of manufacturers' digital pathology products for diagnostic usage, which offers a strong basis for the creation of useful AI solutions. Researchers, physicians, business executives, government agencies, and patient advocacy organizations working together will make it much easier for these cutting-edge technologies to spread and become widely used by medical professionals. These technologies usher in a new age in healthcare, distinguished by their exceptional performance, faster processing rates, cost effectiveness, increased accuracy, and strict safety regulations. AI and computational pathology will continue to develop in harmony, opening the door to a new age of improved healthcare delivery. © 2023 IEEE.

关键词： Artificial Intelligence Cellular Imaging Deep Learning Diagnostic Imaging and Machine Intelligence Diagnostics Digital Pathology Healthcare Pathology

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Service-Centric Architectures in Cloud Security: An In-Depth Analysis

Service-Centric Architectures in Cloud Security: An In-Depth...

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2023 International Conference on Advances in Computation, communication and Information Technology, ICAICCIT 2023

作者： Natya, S. Vivek, V. Yadav, Dhananjay Kumar Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India

ISBN: (纸本)9798350344387

Due to its affordable and high-quality services, cloud computing has lately attracted a lot of interest. Due to the on- demand, pay-per-use characteristics that cloud services provide, which encourage companies to outsource parts of their operations with better service delivery and more value, they have become an essential component of both businesses et people's everyday lives during the last ten years. In the next years, a continued migration towards cloud-based services is predicted by the current market trend, which started in 2019. Despite the many benefits that the cloud computing paradigm offers to both enterprises and consumers, security issues are expected to be the biggest cloud computing challenges in 2020. Security problems are a result of a number of variables, including the streaming nature of these solutions and the technology that support cloud computing, which includes hybridization and multitenancy. This research examines security concerns in service-driven cloud computing in order to shed light on the situation of the industry at the moment. This paper's main contribution is an analysis of cloud security over the last ten years and the presentation of a thorough taxonomy of security concerns throughout the three-layer architecture, which includes Infrastructure-as-a-Service (IaaS), Platform-as- (PaaS), and Software-as-a-Service (SaaS). © 2023 IEEE.

关键词： Cloud computing Hypervisor Infrastructure-as-a-Service (IaaS) Multitenancy Platform-as-a-Service (PaaS) Security issues Service-driven cloud computing Software-as-a-Service (SaaS) and Denial of Service (DoS) Virtualization

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Evolving Mobile Cloud: Enhancement Technologies and Future Approaches

Evolving Mobile Cloud: Enhancement Technologies and Future A...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Mahesh, T.R. Kumar, Ritesh Kehkeshan Jallal, S. Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350357769

There are still limitations in terms of life of the battery and processing power that make it difficult to fulfill the expanding needs of mobile users, despite the fast progress in hardware capacity or the widespread usage of mobile devices. Both conventional and cutting-edge approaches have been investigated to close the distance between customer demands and the constraints of mobile devices. In the context of the mobile cloud, current studies have concentrated on boosting the performance for mobile devices using augmentation approaches. Our emphasis is on the methods themselves, as opposed to current taxonomies in this area, with the aim of establishing a whole mobile computing cloud system. With the goal of ensuring dependable mobile cloud services, this survey may be used as a reference for implementing the augmentation approaches that are now accessible in portable cloud computing systems. We also talk about the unresolved issues and prospective directions for further study in this area. © 2023 IEEE.

关键词： augmentation approaches context sensitivity data interoperability Mobile cloud mobile storage augmentation parallel execution privacy and security and incentive mechanisms reliability resource amplification

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Cloud Deployment Unveiled: A Security Model Focused on Industry

Cloud Deployment Unveiled: A Security Model Focused on Indus...

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2023 International Conference on Advances in Computation, communication and Information Technology, ICAICCIT 2023

作者： Yadav, Rakesh Kumar Natya, S. Mahesh, T.R. Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350344387

The Cloud Compute Adoption Architecture (CCAF), a unique architecture designed to improve safety of the business cloud installations, is introduced in this research article. Using router, authentication and authorization, and encryption methods embedded in Industrial File Sync/Share systems, CCAF aims to provide a multi-layered privacy strategy. Our paper describes the creation of this security A system, examines relevant literature, and explains the reasoning behind CCAF. Through testing and thorough analysis of the key technologies, the study assesses the effectiveness of the CCAF security layered approach. The intricate security system in place at CCAF proved to be an outstanding tool during penetration testing, detecting and eliminating 99.95% of worms including trojans, and even deactivating over 85% of them after 100 hours of continuous attack. Of particular note was the operating system's lightning-fast reaction time of less than 0.012 seconds per trojan and virus, which allowed for the early detection and mitigation of threats. Moreover, each try at SQL injection was successfully thwarted by the CCAF's extensive security defenses, guaranteeing strong security of data. The article highlights the usefulness of CCAF by exploring how its multi-layered security is seamlessly integrated into current regulations, services, and corporate processes. The research highlights how important CCAF's complex security is for Big Data applications running in the cloud, especially when it comes to scalability, speed, and veracity. The study's conclusions highlight CCAF's legitimacy and significant contributions to the field, demonstrating its applicability and effectiveness in protecting the cloud. © 2023 IEEE.

关键词： Cloud Compute Adoption Framework (CCAF) encryption Enterprise File Sync firewall identity management multilayered security approach penetration testing and SQL injection assessment security technologies Share solutions

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Efficient Segmentation for wsn Connectivity Technologies

Efficient Segmentation for wsn Connectivity Technologies

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Vivek, V. Savita Dore, Aruna Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350357769

In wireless sensors networks, the chargeable capacity of sensor nodes serves as the primary energy source. In WSNs, grouping has emerged as a crucial strategy to save energy use and lengthen network lifespan. Transport energy is inversely correlated to the amount of distance across transmitter and destination nodes, which supports clustering. In this paper, we provide a novel fuzzy logic model for the cluster head (CH) choice, a key step in clustering in WSNs. The recommended model includes five characteristics to assess each node's suitability for CH status. The closeness to the starting place, volume, topographical suitability, and remaining power are some of these criteria. This fuzzy logic methodology is used to propose the Fuzzy Reasoning-based Energy-Efficient Grouping for WSN, with emphasis on establishing the shortest possible distance across CHs (FL-EEC/D). As a further metric of clustered methods' energy use, we also evaluate how efficiently they distribute energy among the sensors inside the WSN using the Gini index. We compare our proposed FL-EEC/D approach with the Low Energy Adaptation Clustered Architecture (LEACH), a fuzzy logic-based clustering approach, and various techniques. For various network sizes and topologies, simulation findings show considerable improvements in energy efficiency, network longevity, and balanced energy consumption across sensor nodes. Our results show that initial node depletion and half node depletion have significantly improved on average. © 2023 IEEE.

关键词： Cluster Head Election Clustering Algorithms Energy Efficiency Fuzzy Inference Systems Fuzzy Logic Model Gini Index Measurement Network Longevity Routing Protocols Separation Distance Wireless Sensor Networks

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